Abstract

Despite the occurrence of pool-riffle sequences in many rivers there are few data concerning pool-riffle unit morphology. Of many criteria proposed to identify pool-riffle units, only two methods can be regarded as objective and robust. These are the â��zero-crossingâ�� and the â��control-pointâ�� methods. In this paper statistics are developed from the â��zero-crossingâ�� method to describe the streamwise morphology of 275 riffles and 285 pools which form a near-continuous 32.1km of the bed of the River Severn in Shropshire, England. Yalinâ��s theoretical relationship between pool:riffle unit length (ï�¬p) and channel width (W): ï�¬p = 3W applies to the River Severn. Reach-average riffle height (H) is a constant proportion of bankfull depth (h); typically H ï�� 0.16h. Riffle height is a positive function of riffle length. Pool depth is a positive function of pool length. However both riffle length and pool length increase more rapidly than the bed-level amplitude; such that long riffles or pools are relatively â��flatâ��. As channel gradient reduces, bedforms flatten and become more asymmetric as riffle stoss sides and the proximal slope of pools lengthen at the expense of riffle lee sides and pool distal slopes. The statistical relationships between riffle height, length and water depth are similar to those for equilibrium subaqueous dunes. The Severn data are partially consistent with Yalinâ��s theoretical analysis relating riffle bedform length (Lr) to water depth i.e. Lr = ï�¡2ï�°h wherein ï�¡ ï�� 1 for steep near-equilibrium bedforms but ï�¡ ï�� 2 to 3 as the relative depth decreases and riffles become long-low features. A consideration of turbulence data indicates that the frequency of coherent turbulent-flow structures associated with the riffle-pool mixing length is of the order of 60 seconds. The morphological and dynamic similarity of the River Severn riffles with dunes raises intriguing questions with respect to self-similar, convergent organization of periodic alluvial bedforms and to bedform dynamic classification particularly.